29 m6A-RNA Methylation (Epitranscriptomic) Regulators Are Regulated in 41 Diseases including Atherosclerosis and Tumors Potentially via ROS Regulation – 102 Transcriptomic Dataset Analyses

We performed a database mining on 102 transcriptomic datasets for the expressions of 29 m6A-RNA methylation (epitranscriptomic) regulators (m6A-RMRs) in 41 diseases and cancers and made significant findings: (1) a few m6A-RMRs were upregulated; and most m6A-RMRs were downregulated in sepsis, acute respiratory distress syndrome, shock, and trauma; (2) half of 29 m6A-RMRs were downregulated in atherosclerosis; (3) inflammatory bowel disease and rheumatoid arthritis modulated m6A-RMRs more than lupus and psoriasis; (4) some organ failures shared eight upregulated m6A-RMRs; end-stage renal failure (ESRF) downregulated 85% of m6A-RMRs; (5) Middle-East respiratory syndrome coronavirus infections modulated m6A-RMRs the most among viral infections; (6) proinflammatory oxPAPC modulated m6A-RMRs more than DAMP stimulation including LPS and oxLDL; (7) upregulated m6A-RMRs were more than downregulated m6A-RMRs in cancer types; five types of cancers upregulated ≥10 m6A-RMRs; (8) proinflammatory M1 macrophages upregulated seven m6A-RMRs; (9) 86% of m6A-RMRs were differentially expressed in the six clusters of CD4+Foxp3+ immunosuppressive Treg, and 8 out of 12 Treg signatures regulated m6A-RMRs; (10) immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 modulated m6A-RMRs, and inhibition of CD40 upregulated m6A-RMRs; (11) cytokines and interferons modulated m6A-RMRs; (12) NF-κB and JAK/STAT pathways upregulated more than downregulated m6A-RMRs whereas TP53, PTEN, and APC did the opposite; (13) methionine-homocysteine-methyl cycle enzyme Mthfd1 downregulated more than upregulated m6A-RMRs; (14) m6A writer RBM15 and one m6A eraser FTO, H3K4 methyltransferase MLL1, and DNA methyltransferase, DNMT1, regulated m6A-RMRs; and (15) 40 out of 165 ROS regulators were modulated by m6A eraser FTO and two m6A writers METTL3 and WTAP. Our findings shed new light on the functions of upregulated m6A-RMRs in 41 diseases and cancers, nine cellular and molecular mechanisms, novel therapeutic targets for inflammatory disorders, metabolic cardiovascular diseases, autoimmune diseases, organ failures, and cancers.

RNA carries a spectrum of more than 100 chemical modifications including RNA methylation that play significant roles in the regulation of gene expression [35,36]. As the most dominant mRNA modification, N 6 -methyladenosine (m 6 A), installed onto mRNA by the methyltransferase-like 3 (METTL3)/methyltransferase-like 14 (METTL14) methyltransferase complex, is at a frequency of 0.15-0.6% of all adenosines in polyadenylated RNA [37]. In addition, six other RNA methylations have also been reported such as pseudouridine (Ψ), 5-methylcytidine (m 5 C), N1-methyladenosine (m 1 A), N4-acetylcytidine (ac 4 C), ribose methylations (Nm), and N7-methylguanosine (m 7 G) [38]. At 25-60% of transcriptome, m 6 A methylation regulates gene expression by influencing numerous aspects of mRNA processes of RNA polymerase II-transcribed mRNAs such as pre-mRNA processing, nuclear export, decay, and translation as well as long noncoding RNAs (lncRNAs) [39]. In addition, m 6 A plays an important role on noncoding chromosome-associated regulatory RNAs (carRNAs) for gene expression, which includes enhancer RNAs (eRNAs), promoter-associated RNAs (paR-NAs), and transposable element transcribed RNA (repeat RNAs). Similar to DNA methylation as a mode of epigenomic regulation, the m 6 A methylation that occurred in RNA as a mode of epitranscriptomic regulation becomes important in the crucial roles of m 6 A-mediated gene regulation in many physiological and disease processes [37]. Another important feature of m 6 A methylation is the reversibility, allowing for the regulation of m 6 A levels after initial deposition. Fat mass and obesity-associated protein (FTO) and AlkB Homolog 5 (ALKBH5) have been discovered as m 6 A demethylases (erasers).
The m 6 A methylation also plays a significant role in human disease pathology. Loss of METTL14 has been shown to increase endometrial cancer cells' proliferation and tumorigenicity. In contrast, METTL3/METTL14 have been found to play significant roles in acting as oncogenes in acute myeloid leukemia and glioblastoma and promoting or inhibiting roles in hepatocellular carcinoma [40]. Since METTL3/METTL14 have been indicated to be promising drug targets, phase 1 trials of small-molecule inhibitors for METTL3/METTL14 have been planned for 2021-2022 [37]. In addition, METTL3/METTL14 and m 6 A methylation have been reported to play roles in other diseases such as heart failure, viral infection, type 2 diabetes [37], cardiac remodeling, atherosclerosis, congenital heart disease, inflammation, obesity, insulin resistance, adipogenesis, and hypertension [41]. Moreover, decreased expressions of fat mass and obesity-associated protein (FTO) [42] have been related to heart failure, and overexpression of FTO in failing heart results in decrease of ischemia-triggered loss of cardiac function. Finally, YT521-B homology (YTH, m 6 A-dependent RNA binding) domain family (YTHDF) [43] reader proteins are also involved in pathogenic processes since YTHDF2 is essential for acute myeloid leukemia initiation and leukemia stem cell development [37].
Despite major advancements in the discipline, the following questions remain unanswered: (1) whether the expressions of 29 m 6 A-RMRs are modulated in 41 diseases and cancers including acute inflammation, sepsis, acute respiratory distress syndrome, shock, trauma, cardiovascular diseases (CVDs), autoimmune diseases, and organ failures; (2) whether macrophages and CD4 + Foxp3 + regulatory T cells (Treg) serve as the key cellular mechanisms underlying the roles of m 6 A-RMRs in various diseases and cancers; and (3) whether nine types of molecular mechanisms including danger-associated molecular pattern receptors (DAMP receptors); proinflammatory cytokines; immune checkpoint and costimulation receptors; methionine-homocysteinemethyl donation cycle enzymes; m 6 A-RMRs; proinflammatory transcription factors NF-κB and JAK/STAT; tumor suppressors TP53, PTEN, and APC; histone methyltransferases; and DNA methyltransferases play significant roles in modulating m 6 A-RMRs. After analyzing 102 transcriptomic datasets according to our flow chart (Figure 1), we made significant findings as summarized in Abstract. Our findings reveal new information about the roles of elevated m 6 A-RMRs in the pathogenesis of 41 illnesses and tumors, as well as new therapeutic targets for inflammation, sepsis, trauma, organ failures, autoimmunity, metabolic cardiovascular disorders, and cancers.

Expression Levels of m 6 A-RMRs in Patients with Various
Inflammatory Disorders and Tumors. We collected 23 microarray datasets of acute inflammation, metabolic and 2 Journal of Immunology Research cardiovascular diseases, autoimmune diseases, and organ failures (Table 1); one microarray of Middle-East respiratory syndrome (MERS) coronavirus-infected human microvascular endothelial cells; one microarray dataset (nine comparisons) of subacute respiratory syndrome coronavirus-(SARS-CoV-) infected human airway epithelial cells; one microarray dataset (23 comparisons) of influenza virusinfected lung epithelial cells (Table 2); and eight microarray datasets of endothelial cells (Table 3) from National Institutes of Health-(NIH-) National Center for Biotechnology Information-(NCBI-) Gene Expression Omnibus (GEO) databases (https://www.ncbi.nlm.nih.gov/gds/). These datasets were analyzed with GEO2R (https://www.ncbi.nlm.nih .gov/geo/geo2r/). Some datasets were overlapped with our previous studies [44]. In addition, the Oncomine database (https://www.oncomine.org) was used to analyze the gene expression profile from 19 tumors [45], with threshold parameters of fold change > 2, p < 0:05, and gene rank in the top 10%. Because these microarray studies employed diverse cell types, we were unable to compare the effects of illness circumstances on m 6 A-RMR regulation in the same cell types. It is worth noting that our strategy was well justified. For example, we and others frequently investigated gene expression in nonideal heterogeneous peripheral blood mononuclear cell populations (PBMCs) in pathophysiologi-cal conditions, which are made up of a variety of cell types (also see Discussion).

Expression
Profile of m 6 A-RMRs in Single-Cell RNA Sequencing (scRNA-Seq) Datasets from Studies of Sepsis, Atherosclerosis, Tumors, and Endothelial Cell. Five scRNA-Seq datasets were collected from the Single Cell Portal database (https://singlecell.broadinstitute.org/single_cell), including one study about sepsis, one study about atherosclerosis, two studies about tumors (astrocytoma and melanoma), and one study about endothelial cell populations (Supplementary Table 1). The expressions of 29 m 6 A-RMRs were online analyzed.

Expression Regulation Analysis of m 6 A-RMRs from
Deficiency of Folate Cycle and Metabolism-Related Enzymes, m 6 A-RMRs, H3K4 Methylase, DNA Methyltransferase, Regulatory T Cells' Signature Genes, Proinflammatory Cytokines, Oncogene, Tumor Suppressors, and Immune Checkpoint Receptors. The 68 microarray datasets in the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih .gov/gds/) were collected in analyzing the regulatory mechanisms of m 6 A-RMRs (Supplementary Table 2). There are six microarrays about deficiencies of the folate cycle and metabolism-related enzymes and m 6 Figure 1: Flow chart of the study. Data mining work includes two parts: (I) the expression changes of m 6 A-RMRs in diseases including acute inflammation, sepsis, acute respiratory distress syndrome, shock, trauma, cardiovascular diseases (CVDs), autoimmune diseases, organ failures, and cancers were examined; (II) cellular mechanisms including macrophages and CD4 + Foxp3 + regulatory T cell (Treg) modulation and molecular mechanisms including the role of danger-associated molecular pattern receptors (DAMP receptors); proinflammatory cytokines; immune checkpoint and costimulation receptors; methionine-homocysteine-methyl donation cycle enzymes; m 6 A-RMRs; proinflammatory transcription factors NF-κB and JAK/STAT; tumor suppressors TP53, PTEN, and APC; histone methyltransferases; and DNA methyltransferases in regulation of m 6 A-RMRs were explored.  6 A-RMRs with expression changes more than 1-fold (red) were defined as the upregulated genes, while genes with expression decreased more than 1-fold  ) including YTHDC1, YTHDF1,  YTHDF2, YTHDF3, YTHDC2, HNRNPA2B1, EIF3A,  IGF2BP1, IGF2BP2, IGF2BP3, and FMR1; (4) three m 6 Adependent RNA binding proteins including HNRNPC, RMMX, and PRRC2A; and (5) three m 6 A-repelled RNA binding proteins including ELAVL1, G3BP1, and G3BP2. As shown in Figure 3(a), one to four out of 28 m 6 A-RMRs were upregulated; and 14 to 15 out of 28 m 6 A-RMRs were downregulated in 0 day and 7 days in patients with sepsis T r a n s l a t i o n Storage D ec ay   [46], respectively. In the second datasets with sepsis and sepsis plus shock, two to four out of 26 m 6 A-RMRs were upregulated; and 10 to 17 out of 26 m 6 A-RMRs were downregulated in 1 day and 3 days in patients with sepsis and sepsis plus shock [47,48], respectively. In the third datasets with leukocytes, monocytes, and T cells from trauma, zero to one out of 22 m 6 A-RMRs was upregulated; and 5 to 11 out of 22 m 6 A-RMRs were downregulated in leukocytes, monocytes, and T cells from trauma [49], respectively.
We then analyzed the shared and disease-specific m 6 A-RMRs using Venn diagram analysis. As shown in Figure 3 (b), four diseases including sepsis at 0 day and 7 days and sepsis plus ARDS at 0 day and 7 days shared one RNA methyltransferase WTAP. In the second sepsis datasets, four diseases including sepsis at 1 day and 3 days and sepsis plus shock at 0 day and 7 days shared one RNA methyltransferase PCIF1 and one RNA methylation reader IGF2BP3. In the trauma datasets, three immune cell types, leukocytes, monocytes, and T cells did not share any upregulated m 6 A-RMRs. When comparing three groups of acute  The methyl group is transferred to proteins, nucleic acids, and other biochemicals from the biochemical reaction of S-adenosylmethione (SAM) to S-adenosylhomocysteine (SAH) in homocysteine-methionine metabolic cycle. For RNA methylation, mRNA, lncRNA, tRNA, SnRNA, pre-miRNA, and CirRNA can be methylated in many different ways. In addition to the basic methylated forms, the common methylated forms include N6-methyladenosine (m 6 A), N1-methyladenosine, N1-methyladenosine, 2′-O-methyls at mRNA, N6-methyladenosine at lncRNA, pre-miRNA, circRNA [50][51][52][53][54][55], N1-methyladenosine at tRNA, 2′-O-methyls at rRNA, and snRNA [56]. (b) The reported 29 m 6 A-RMRs were chosen for analysis, including 10 m 6 A methyltransferase enzymes (writers), two m6A demethylases (erasers), 11 m 6 A binding proteins (readers), three m 6 A dependent on RNA binding proteins, and three RNA binding proteins repelled by m 6 A. The detailed information including the reference articles of the 29 m 6 A-RMRs is listed in Supplementary Table 3. (c) The methyl donor is derived from folate cycle and coupled homocysteine-methionine cycle. When the donor source is impaired, the DNA, RNA, and protein methylations will be in disorder. We proposed the hypothesis: mRNA methylation disorders will affect the expressions of m 6 A-RMRs; then, the m 6 A-RMRs will be involved in inflammation, metabolic diseases, and tumors by methylating or demethylating target genes including RNAs.    inflammations, sepsis plus ARDS shared three upregulated m 6 A-RMRs such as writers WTAP, PCIF1, and reader YTHDF3 with sepsis plus shock; sepsis plus ARDS shared upregulation of one RNA binding protein IGF2BP2 with trauma, and sepsis plus shock shared one RNA binding protein IGF2BP3 with trauma. We noticed that in the trauma datasets ( Figure 3(b)), three immune cell types, leukocytes, monocytes, and T cells did not share any upregulated RNA methylation regulators, suggesting that upregulation of m 6 A-RMRs is in a cellspecific manner. To further examine this issue, we collected single-cell RNA-sequencing (scRNA-Seq) datasets in sepsis [57] in a comprehensive single-cell sequencing database (https://singlecell.broadinstitute.org/single_cell). As shown in Figure 3 These results have demonstrated that first, a few m 6 A-RMRs are upregulated and most m 6 A-RMRs are downregulated in sepsis, sepsis plus acute respiratory distress syndrome, sepsis plus shock, and trauma; second, two RNA methyltransferases WTAP and PCIF1 and three RNA binding proteins such as YTHDF3, IGF2BP2, and IGF2BP3 are commonly upregulated in sepsis, sepsis plus ARDS, sepsis plus shock, and trauma, suggesting that these five m 6 A-RMRs are the emergency m 6 A-RMRs for promoting acute inflammatory diseases; third, nine m 6 A-RMRs including METTL3, RBM15, FTO, YTHDC2, HNRNPA2B1, EIF3A, HNRNPC, G3BP1, and CBLL1 are commonly downregulated in sepsis, sepsis plus ARDS, sepsis plus shock, and trauma, suggesting that those m 6 A-RMRs play more important roles in maintaining homeostasis and suppressing inflammation than emergency roles for acute inflammations; and fourth, the expressions of 38% m 6 A-RMRs in immune cell types in response to sepsis stimulation are different.

Type 2 Diabetes Has More Modulation of m 6 A-RMR Expressions Than Atherogenic Diseases and Obesity; Nearly
Half of 29 m 6 A-RMRs Are Downregulated as Atherosclerosis Progression Compared with That of Atherosclerosis Regression. We hypothesized that major metabolic cardiovascular disease groups such as obesity [58,59], type 2 diabetes, and atherogenic diseases differentially modulate the expressions of m 6 A-RMRs. To test this hypothesis, we collected five datasets of obesity including obese, metabolically unhealthy obesity (MUO), metabolically healthy obesity (MHO), obese with insulin resistance (ob IR), and obese with insulin sensitivity (ob IS); four datasets of type 2 diabetes; and five datasets of atherosclerosis, familial hypercholesterolemia (FHC) plus atherosclerosis, and familial combined hyperlipidemia (FCH) from the NCBI-GeoDataset (https://www.ncbi.nlm.nih.gov/gds/). As shown in Figure 4(a), in five diseases in the obesity group, zero to three m 6 A-RMRs were upregulated and zero to 10 m 6 A-RMRs were downregulated. However, in the four type 2 diabetes datasets, zero to 11 m 6 A-RMRs were upregulated and one to seven m 6 A-RMRs were downregulated. The five datasets of atherosclerotic diseases had modulations of m 6 A-RMRs higher than those in the obesity group but lower than those of type 2 diabetes group: zero to seven m 6 A-RMRs were upregulated, and zero to seven m 6 A-RMRs were downregulated.
We also analyzed the shared and disease-specific m 6 A-RMRs using Venn diagram analysis (Figure 4(b)). In six m 6 A-RMRs upregulated in the obesity group, two regulators IGF2BP3 (RNA methyltransferase) and G3BP1 (m 6 A repelled RNA binding protein) were shared by obesity IS and obesity IR. Among the 15 m 6 A-RMRs upregulated in four different tissues in type 2 diabetes, one regulator HNRNPA2B1 (reader) was shared by liver, subcutaneous adipose, and visceral adipose, and one regulator G3BP1 was shared by liver and visceral adipose. In 10 m 6 A-RMRs upregulated in atherosclerotic diseases, one regulator WTAP (RNA methyltransferase) was shared by atherosclerosis (athero) carotid artery and atheromacrophages, and two regulators PCIF1 (RNA methyltransferase) and PRRC2A (m 6 A dependent RNA binding protein) were shared by atheromacrophages and FHC and atheromonocytes. Among 21 m 6 A-RMRs upregulated in three major metabolic cardiovascular disease groups, one regulator WTAP (RNA methyltransferase) was shared by three major groups; one regulator IGF2BP3 was shared by obesity and athero; three regulators FTO (demethylase), YTHDF2 (RNA binding protein), and G3BP1 were shared by obesity and type 2 diabetes; and four regulators such as PCIF1 (RNA methyltransferase), PRRC2A (m 6 A-dependent RNA binding protein), YTHDC2 (RNA binding protein), and HNRNPC (m 6 A-dependent RNA binding protein) were shared by type 2 diabetes and atherosclerotic diseases.
We then examined a hypothesis that atherosclerosis progression and regression differentially modulate the expressions of m 6 A-RMRs. We collected a dataset of scRNA-Seq in the scRNA-Seq database in the Broad Institute at MIT. As shown in Figure 4(c), the expressions of 14 out of 27 m 6 A-RMRs including Wtap, Pcif1, Alkbh5, Ythdc1, Ythdf1, Ythdf2, Ythdf3, Hnrnpa2b1, Eif3a, Fmr1, Hnrnpc, Prrc2a, G3bp1, and G3bp2 were decreased in the progressive atherosclerosis compared with those in the regressive atherosclerosis. The expression of Elavl1 was increased in the progressive atherosclerosis compared with that in the regressive atherosclerosis. Of note, the expressions of Virma and Igf2bp1 were not found. These results have illustrated that (1) atherosclerotic macrophages have higher upregulation of m 6 A-RMRs than atherosclerotic carotid artery, and FHC and atherosclerotic monocytes have more modulation of m 6 A-RMRs than FHC and atherosclerotic T cells; (2) type 11 Journal of Immunology Research   Function   m6A  methylation  regulator   GSE48964 GSE55200 GSE55200 GSE94752 GSE94752 GSE23343 GSE29221 GSE29226 GSE29231 GSE28829 GSE41571 GSE1010 GSE6054 GSE6088   Obese  MHO  MUO  ob IR  ob IS  T2D  T2D  T2D  T2D  Athero  Athero  FCH  FHC and  Athero   FHC and  Athero  FC  FC  FC  FC  FC  FC  FC  FC  FC  FC  FC  FC  FC  Obesity (6) T2D (15) Athero (10) 1 IGF2BP1 Obesity (15) T2D (12) Athero (12)  G3BP2  G3BP1  ELAVL1  PRRC2A  RBMX  HNRNPC  FMR1  IGF2BP3  IGF2BP2  EIF3A  HNRNPA2B1  YTHDC2  YTHDF3  YTHDF2  YTHDF1  YTHDC1  ALKBH5  FTO  PCIF1  METTL16  RBM15B  RBM15  ZC3H13  CBLL1  WTAP  METTL14     .gov/gds/). In a RA dataset, four out of 26 m 6 A-RMRs were upregulated; and 10 out of 26 m 6 A-RMRs were downregulated. In four lupus datasets, two to six out of 28 m 6 A-RMRs were upregulated; and one to four out of 28 m 6 A-RMRs were downregulated. In three colitis datasets, five to eight m 6 A-RMRs were upregulated; and eight to nine m 6 A-RMRs were downregulated ( Figure 5). We also analyzed the shared and disease-specific m 6 A-RMRs using Venn diagram analysis. In seven m 6 A-RMRs upregulated in the autoimmune skin disease group, one m 6 A-RMR WTAP was shared by four autoimmune skin diseases; one m 6 A-RMR G3BP1 was shared by three autoimmune skin diseases including ACLE, CCLE, and psoriasis; one m 6 A-RMR PRRC2A was shared by three autoimmune skin diseases including CCLE, psoriasis, and SCLE; and one m 6 A-RMR G3BP2 was shared by two autoimmune skin diseases including CCLE and psoriasis. In 13 m 6 A-RMRs upregulated in the inflammatory bowel disease group, one m 6 A-RMR ELAVL1 was shared by UC sigmoid colon or ACLE (2) CCLE (5) Psoriasis (6) SCLE (2) 1 WTAP  (4) Autoimmune skin disease (7) Inflammatory bowel disease (13) Rheumatoid Arthritis (10) Autoimmune skin disease (5) Inflammatory bowel disease (16) 1 RBM15B Figure 5: The m 6 A-RNA methylation regulators (m 6 A-RMRs) were more differentially expressed in rheumatoid arthritis (RA) and autoimmune skin diseases than those in other autoimmune diseases. (a) The expression changes of m 6 A-RMRs showed the higher expression fold changes in RA and skin autoimmune diseases than those in inflammatory bowel disease. KIAA1429 and PCIF1 were highly increased; and METTL14, CBLL1, and RBM15 were highly decreased in RA; WTAP, PRRC2A, and G3BP2 were highly increased genes in skin autoimmune disease psoriasis. (b) Venn diagram showed that there were seven upregulated m 6 A-RMRs and five downregulated m 6 A-RMRs in autoimmune skin diseases. The writer WTAP and reader HNRNPA2B1 were the common increased and decreased genes in autoimmune skin diseases. There were 13 upregulated m 6 A-RMRs and 16 downregulated m 6 A-RMRs in inflammatory bowel diseases. YTHDC2 and G3BP2 were the common downregulated m 6 A-RMRs. PCIF1 was the shared upregulated m 6 A-RMR by RA and inflammatory bowel disease. CBLL1, RBM15, YTHDF3, and EIF3A were the shared downregulated m 6 A-RMRs by RA and inflammatory bowel disease. There were 19 upregulated m 6 A-RMRs and 21 downregulated m 6 A-RMRs in these three types of autoimmune diseases. Note: the red marked genes are those that are up-or downregulated in different diseases (p < 0:05 We collected four microarray datasets from the NCBI-GeoDatasets (https://www.ncbi.nlm.nih.gov/gds/) to examine this hypothesis. As shown in Figure 6, in the heart failure dataset, six out of 26 m 6 A-RMRs were upregulated; and seven out of 26 m 6 A-RMRs were downregulated. In the HBV-ALF dataset, eight out of 26 m 6 A-RMRs were upregulated; and 17 out of 26 m 6 A-RMRs were downregulated. In the ESRF dataset, four out of 26 m 6 A-RMRs were upregulated; and 22 out of 26 m 6 A-RMRs were downregulated. In the hemodialysis dataset, 10 out of 22 m 6 A-RMRs were upregulated; and five out of 22 m 6 A-RMRs were downregulated. We also analyzed the shared and disease-specific m 6 A-RMRs using Venn diagram analysis. In 20 m 6 A-RMRs upregulated in four organ failure groups, one upregulated m 6 A-RMR (RBM15B, RNA methyltransferase) was shared by HF and HBV-ALF; one upregulated m 6 A-RMR (PRRC2A) was shared by HF and ESRF; two upregulated m 6 A-RMRs (IGF2BP2 and IGF2BP3, RNA binding proteins) were shared by HBV-ALF and ESRF; and four m 6 A-RMRs including WTAP (RNA methyltransferase), RBM15 (RNA methyltransferase), PCIF1 (RNA methyltransferase), and HNRNPC (m 6 A-dependent RNA binding protein) were shared by HBV-ALF and hemodialysis. These results demonstrated that first, four major organ failures have no commonly shared upregulated m 6 A-RMRs but share eight m 6 A-RMRs between groups including RBM15B, IGF2BP2, IGF2BP3, PRRC2A, WTAP, RBM15, PCIF1, and HNRNPC; second, hemodialysis is the only organ failure that upregulates more m 6 A-RMRs than downregulates m 6 A-RMRs; other organ failures have less upregulation of m 6 A-RMRs than downregulation of m 6 A-RMRs; third, surprisingly, ESRF and hemodialysis have no any shared upregulated m 6 A-RMRs, suggesting that the clinical improvements of hemodialysis from ESRF are benefited from upregulation of m 6 A-RMRs by hemodialysis; and fourth, ESRF modulates 100% of 26 m 6 A-RMRs, which is the highest modulation rate found among all the diseases examined.   hours resulted in upregulation of two to three m 6 A-RMRs (with the high upregulation of writer WTAP) and downregulation of one to two m 6 A-RMRs, respectively. In addition, cytokine interferon-α (IFNα), IFNβ, and IFNγ treatments of HUVEC led to upregulation of zero to one and downregulation of one to five out of 22 m 6 A-RMRs, respectively. The knockdown (KD) of proinflammatory NOTCH1 signaling and NOTCH1 KD plus proinflammatory cytokine interleukin-1β (IL-1β) in HUVEC led to upregulation of seven and five out of 25 m 6 A-RMRs and downregulation of five and six out of 25 m 6 A-RMRs. The differences of m 6 A-RMR expression modulation between NOTCH1 KD and NOTCH1 KD plus IL-1β may contribute to the inflammatory status of treated HUVEC [66]. In contrast, another report found that NOTCH1 is antiatherogenic; and proinflammatory lipids oxidized 1-palmitoyl-2-arachidonoyl-snglycero-3-phosphocholine (Ox-PAPC) decrease NOTCH1 expression in human aortic endothelial cells (HAEC) [67]. In this experimental setting, NOTCH1 KD in HAEC resulted in upregulation of five and downregulation of 10 out of 25 m 6 A-RMRs; proinflammatory lipid oxPAPC stimulation in HAEC led to upregulation of 10 and downregulation of seven out of 25 m 6 A-RMRs. It was reported that oscillatory shear (OS) present on the fibrosa stimulates fibrosa human aortic valve endothelial cells (HAVEC), which may contribute to aortic valve disease [68]. In this experimental setting, oscillatory shear on fibrosa HAVEC and ventricularis HAVEC resulted in upregulation of four HF (6) HBV-ALF (8) ESRF (4) Hemodialysis ( (7) HBV-ALF (17) ESRF (22) Hemodialysis (5)       In KSHV-infected ECs, IGF2BP3 was downregulated with low FC values. In LPS-treated microvascular ECs, writer WTAP was upregulated in four and eight hours. In IFNa-and IFNb-treated cells, FMR1 was increased. In the cells with NOTCH1 knockdown combined with IL-1β treatment, the expression changes of m 6 A-RMRs were not obvious. ECs activated with ox-PAPC showed that the expressions of WTAP and CBLL1 were upregulated. There were gentle expression changes of m 6 A-RMRs for ECs exposed to shear or for mouse aortic ECs from apolipoprotein E-(ApoE-) deficient (ApoE knock out) or treated with LPS, oxLDL, and oxPAPC, respectively. and six and downregulation of two and three out of 28 m 6 A-RMRs, respectively. Moreover, mouse aortic endothelial cells (MAEC) isolated from atherogenic apolipoprotein Edeficient (ApoE -/-) mice [3], TLR4 ligand LPS-treated MAEC, another TLR4 ligand oxidized low-density lipoprotein-(oxLDL-) stimulated MAEC [69,70], and oxPAPCstimulated MAEC [71] upregulated two, two, five, and eight out of 21 m 6 A-RMRs and downregulated five, six, three, and five out of 21 m 6 A-RMRs, respectively. We then examined the expressions of m 6 A-RMRs in three mouse aortic endothelial cell (EC) clusters identified recently with single-cell RNA sequencing (scRNA-Seq) [72]. As shown in Figure 8(b), 12 out of 26 m 6 A-RMRs (46%) including Wtap, Zc3h13, Pcif1, FTO, Alkbh5, Ythdc1, YthDf2, Ythdf3, Fmr1, Prrc2a, Elavl1, and G3bp1 were in medium expression levels in almost all aortic cell populations of normal mouse aortas; and two m 6 A-RMRs such as Hnrnpa2b1 and Eif3a were differentially expressed in three aortic EC clusters, which were Cytl1 + Gkn3 + endothelial cell (EC) cluster 1, Fabp4 + Gphbp1 + Rgcc + EC cluster 2, and Ccl21a + Lrg1 + EC cluster 3 [72]. Of note, the expressions of Mettl14, Igf2bp1, and Igf2bp2 were not found.
These results conclude that (1) Figure 7; (2) proinflammatory lipids oxPAPC and NOTCH1 knockdown modulate m 6 A-RMRs more than other DAMP stimulation of ECs including LPS, oxLDL, and IFNs; (3) ten m 6 A methyltransferases were not significantly modulated in MAEC from ApoE KO aorta with or without further stimulations of LPS, oxLDL, and oxPAPC, respectively (Figure 8, GSE39264); and (4) 12 out of 26 m 6 A-RMRs (46%) were in medium expression levels in almost all aortic cell populations of normal mouse aortas; and two m 6 A-RMRs including Hnrnpa2b1 and Eif3a were differentially expressed in three aortic EC clusters.  [45] including brain and CNS cancer, head and neck cancer, esophageal cancer, gastric cancer, bladder cancer, breast cancer, cervical cancer, ovarian cancer, colorectal cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, prostate cancer, leukemia, lymphoma, melanoma, myeloma, sarcoma, and other cancer (Figure 9(a)). As shown in Figure 9, the numbers in red indicated the numbers of studies with upregulated m 6 A-RMRs; and the numbers in blue indicated the numbers of studies with downregulated m 6 A-RMRs. The results showed that upregulated m 6 A-RMRs were more than downregulated m 6 A-RMRs in various cancer types (number of red cells is more than that of blue cells). Of note, the "Significant Unique Analyses" indicated the numbers of studies in which the analyzed genes were significantly upregulated (red) or downregulated (blue). The "Total Unique Analysis" indicated the numbers of studies in which the analyzed genes were included (p < 0:05, fold change > 2). Three m 6 A-RMRs such as METTL16, YTHDC1, and PRRC2A were not found.

Upregulated m 6 A-RMRs
We then determined the top cancer types that upregulated or downregulated m 6 A-RMRs the most. As shown in Figures 9(b) and 9(c), head and neck cancer, cervical cancer, brain and CNS cancer, other cancer, and kidney cancer upregulated ≥10 m 6 A-RMRs. In contrast, breast cancer, ovarian cancer, kidney cancer, esophageal cancer, leukemia, and lymphoma downregulated ≥four m 6 A-RMRs. In addition, lymphoma, leukemia, other cancer, sarcoma, and brain and CNS cancer modulated ≥five m 6 A-RMRs in uncertain manners. We also determine the top m 6 A-RMRs that upregulated or downregulated in cancer types the most. As shown in Figures 9(d) and 9(e), IGF2BP3, G3BP1, IGF2BP2, and HNRNPC were upregulated in ≥10 cancer types. In contrast, ZC3H13, IGF2BP1, WTAP, FTO, and YTHDC2 were downregulated in ≥four cancer types. In addition, G3BP2, WTAP, PCIF1, HNRNPA2B1, EIF3A, and IGF2BP2 were modulated in ≥three cancer types in uncertain manners.
The results indicated that most m 6 A-RMRs were downregulated in acute inflammatory diseases, while in cancer, the upregulated m 6 A-RMRs were more than downregulated m6A-RMRs. This situation is because acute inflammation is a part of an innate immune system reaction that can be produced by various factors, such as signaling pathways of the receptors for danger-associated molecule patterns (DAMPs/PAMPs) derived from pathogens, viruses, bacteria, and toxic substances as well as cytokine signals and stress hormone signal. These factors may activate the interactions and cross-talks among the receptors of cytokines, viruses, DAMPs, or PAMPs and promote the migration of macrophages or neutrophils to the area of inflammation [73]. However, during cancer occurs, tumor cells will generate tumor antigens that activate adaptive immune responses and induce T cells and B cells. T cell activation could cause a variety of immune signaling, including T cell antigen receptor signaling, costimulation signaling, immune checkpoint coinhibition signaling, and cytokine signaling. The composition of signaling receptors and pathways is different between acute inflammation and cancer. Therefore, RNA methylation and its transcription, splicing, and mRNA stability are also different under these two situations.
We further hypothesized that m 6 A-RMRs were differentially expressed as different cancer progression. To examine this hypothesis, the expression matrix GSE114783 was downloaded from the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds); and the expressions of m 6 A-RMRs were screened. The heat map was generated from Cluster web tool [45]. As shown in Supplementary figure 1A, the results showed that the expressions of m 6 A-RMRs were different among healthy controls, hepatitis B virus carriers, and the patients with liver cirrhosis and    -regulated  down-regulated  uncertain  IGF2BP3  12  ZC3H13  6  G3BP2  5  G3BP1  11  IGF2BP1  5  WTAP  3  IGF2BP2  10  WTAP  4  PCIF1  3  HNRNPC  10  FTO  4  HNRNPA2B1  3  RBM15  8  YTHDC2  4  EIF3A  3  HNRNPA2B1  8  METTL3  3  IGF2BP2  3  EIF3A  7  IGF2BP2  3  CBLL1  2  ELAVL1  7  FMR1  3  IGF2BP3  2  METTL3 6 Taken together, these results have demonstrated that first, upregulated m 6 A-RMRs were more than downregulated m 6 A-RMRs in various cancer types; second, head and neck cancer, cervical cancer, brain and CNS cancer, other cancer, and kidney cancer upregulated ≥10 m 6 A-RMRs; third, IGF2BP3, G3BP1, IGF2BP2, and HNRNPC were upregulated in ≥10 cancer types; fourth, four m 6 A-RMRs such as HNRNPA2B1, IGF2BP2, FMR1, and HNRNPC were upregulated in patients with preinvasive and invasive cervical squamous cell carcinomas; and the expressions of two m 6 A-RMRs such as EIF3A and RBMX were upregulated in prostate carcinoma samples; and fifth, 82-83% of 29 m 6 A-RMRs were in the medium to high expression levels in astrocytoma and melanoma.

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Journal of Immunology Research M2 polarization-downregulated m 6 A-RMRs such as WTAP and IGF2BP2 may not only promote M1 polarization but also inhibit M2 polarization. Macrophages are diverse immune cells polarized by numerous stimuli, resulting in a wide range of traits and functions. The polarized process from M0 to M1 is induced by the stimulations of TLR ligands or Th1 cytokines, like TNF-a, IFN-γ, and CSF2 signaling. Under inflammation, these macrophage polarization signals are specific and robust [77], whereas, during tumorigenesis, so many factors can be involved. Moreover, different cancers are related to different cell types. A great amount of different signaling cross-talks may cause chaos signaling [78]. Consequently, the signaling that modulates the upregulation of WTAP could be diffused by the chaos cross-talking signaling.

Discussion
Recent progress has reported that m 6 A-RNA methylation [50] plays a significant role in regulating cardiovascular diseases (CVD) and CVD-related diseases and complications such as cardiac remodeling, atherosclerosis, heart failure, inflammation adipogenesis, obesity, insulin resistance, hypertension, and type 2 diabetes mellitus [41]. In addition, m 6 A-RNA methylation plays critical roles in the pathogenesis of cancers and tumors [106,107], aging [107], immune responses and autoimmunity, and viral infections [108,109]. However, two major issues remain unknown: first, transcriptomic regulation of a complete list of m 6 A-RNA methylation regulators in various diseases and second, cellular mechanisms and molecular mechanisms underlying transcriptomic changes of m 6 A-RNA methylation regulators in pathophysiological conditions. To solve these problems, we performed a transcriptomic data mining of the expressions of 29 m 6 A-RNA methylation regulators in diseases and cancers and made significant findings: (1) a few m 6 A-RMRs were upregulated; and most m 6 A-RMRs were downregulated in sepsis, acute respiratory distress syndrome, shock, and trauma; (2) half of 29 m 6 A-RMRs were downregulated in atherosclerosis progression compared with those of regression; (3) IBD and RA modulated m 6 A-RMRs more than lupus and psoriasis; and some autoimmune diseases share five upregulated m 6 A-RMRs; (4) some organ failures shared eight upregulated m 6 A-RMRs; end-stage renal failure (ESRF) downregulated 85% of m6A-RMRs; and upregulation of m 6 A-RMRs in hemodialysis more than in ESRF may have clinical benefits; (5) MERS-CoV infections modulated m 6 A-RMRs the most among viral infections; (6) oxPAPC and NOTCH1 knockdown modulated m 6 A-RMRs more than other DAMs stimulation of endothelial cells including LPS, oxLDL, and IFNs; (7) upregulated m 6 A-RMRs were more than downregulated m 6 A-RMRs in cancer types; five types of cancers upregulated ≥10 m 6 A-RMRs; (8) M1 macrophages upregulated seven m 6 A-RMRs; WTAP and IGF2BP2 may not only promote M1 but also inhibit M2 polarization; (9) 86% of m 6 A-RMRs were differentially expressed in the six spleen Treg clusters; and 8 out of 12 Treg signatures significantly regulated m 6 A-RMRs; (10) immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 significantly modulated m 6 A-RMRs; and inhibition of costimulation receptor CD40 with anti-CD40 significantly upregulated m 6 A-RMRs; (11) proinflammatory cytokines significantly modulated m 6 A-RMRs; (12) NF-κB and JAK/ STAT pathways (except STAT1) upregulated more than downregulated m 6 A-RMRs; and TP53, PTEN, and APC 35 Journal of Immunology Research downregulated more than upregulated m 6 A-RMRs; (13) methionine-homocysteine cycle enzyme Mthfd1 downregulated more than upregulated m 6 A-RMRs; m 6 A writer RBM15 and one m 6 A eraser FTO significantly modulated m 6 A-RMRs; and H3K4 methyltransferase MLL1 and DNA methyltransferase, DNMT1, significantly regulated m 6 A-RMRs; and (14) 40 out of 165 ROS regulators were modulated by m 6 A eraser FTO and two m 6 A writers METTL3 and WTAP.
For some diseases, both eraser and writing enzymes of m 6 A-RMRs have changed or have the same expression trend. In order to explain this phenotype, we checked the m 6 A-RMRs changes in Met-DB v2.0 and the result supports m 6 A-RMRs regulate expression of m 6 A-RMRs themselves (Supplementary Tables 5 and 6). When the writer METTL3 or WTAP was knocked down, the expression of eraser ALKBH5 also was down regulated (Supplementary  Table 5). Then, Protein-Protein Interaction (PPI) analysis was performed by using STRING (https://string-db.org/) and the result also suggest the interactions among m 6 A-RMRs are complex (Supplementary Figure 3). Additionally, the expression of two kinds of m 6 A-RMRs such as writer WTAP and eraser FTO is positively correlated in some tumors (Supplementary Figure 4). So, in some diseases, writers and erasers both are upregulated or downregulated, which is reasonable and the main function of m 6 A-RMRs can be confirmed by using some well-designed experiments.
One of the potential issues related to database mining is that we were unable to compare the impact of different regulators in controlling the expressions of m 6 A-RMRs in the  , and the result showed 18 ROS  regulators (F2RL1, PDK4, TIGAR, BCL2, SESN2, GNAI2, DDIT4, SH3PXD2A, FOXM1, AATF, TGFB1, TSPO, G6PD, GNAI3, and  CYP1B1) were modulated by writers KIAA1429, METTL14, METTL3, and WTAP (several ROS regulators were modulated in more than one position in the chromosome or by more than one RNA methylation regulator). The deficiencies of writers (KIAA1429, METTL14, METTL3, and WTAP) can downregulate the m6A modification of ROS regulators (p adj < 0:05). Met-DB v2.0 contains a significant increase in context-specific m 6 A peaks and single-base sites predicted from 185 samples from 26 separate studies for 7 species. It has also been updated to include a new database for targets of m 6 A readers, erasers, and writers, as well as additional functional data gathering. The abbreviation TREW stands for Target of m 6 A Readers, Erasers, and Writers. To discover their target sits, we collected ParCLIP-seq and MeRIP-seq data for 8 regulator/reader proteins (including FTO, KIAA1429, METTL14, METTL3, WTAP, HNRNPC, YTHDC1, and YTHDF1) from 10 independent studies. Then, the differential m6A peaks that showed significant hypermethylation (hypomethylation) after knocking down of a demethylase (methylase) were determined to the target peaks. p_treat: peak of treated group; p_control: peak of control group; OR: odds ratio; RR: relative risk or risk ratio. 36 Journal of Immunology Research In folate cycle, transsulfuration pathway, glutathione synthesis, polyamine metabolism, and methionine salvage pathway, homocysteine-methionine cycle serves as a sensor-receptor system to sense the intracellular metabolic homeostasis and stresses of four amino acids such as methionine, homocysteine, serine, and arginine as well as vitamin B12 and folate. The metabolic homeostasis and stress signals relay the metabolic reprogramming signals into cellular methylation processes via various methyltransferases to methylate DNAs, proteins, histones, RNAs, and other molecules. (4) Cell surface receptors such as cytokine receptors, viral receptors, danger-associated molecular pattern (DAMPs) receptors/pathogen-associated molecular pattern (PAMPs) receptors, immune checkpoint receptors, and cosignaling receptors regulate the transcriptomic changes of m 6 A-RMRs. Nuclear transcription factors (TFs) including proinflammatory TFs NF-κB, Jak-STATs, tumor suppressors TP53, PTEN, and APC regulate the transcriptomic changes of m 6 A-RMRs. The figure was created with http://BioRender.com. 37 Journal of Immunology Research same cell types since the original microarray studies we looked at employed different cells. Although our database mining strategy was not optimal, our approach was justified in filling up a critical knowledge gap. This is, in fact, a common practice that we and others [110] often use in studying gene expression in nonideal, heterogeneous peripheral blood mononuclear cell populations (PBMCs) in disease conditions versus healthy conditions, and PBMCs are actually made up of a variety of cell types, including B cells (~15%), T cells (~70%), monocytes (~5%), and natural killer (NK) cells (~10%) among others [111]. Another limitation of the current study is that, due to the low-throughput nature of verification techniques in every laboratory, including ours, we were unable to confirm every result we uncovered using high-throughput data analyses. We recognize that in the future, carefully designed in vitro and in vivo experimental models will be required to confirm regulator gene deficiency-upregulated m6A-RMRs further and the underlying mechanisms we disclose here.
Based on our findings, we proposed a novel working model in Figure 16. First, we recently proposed a new theory that because of their connections with three metabolic pathways including folate cycle, transsulfuration pathway, glutathione synthesis, polyamine metabolism, and methionine salvage pathway, homocysteine-methionine cycle serves as a sensor-receptor system to sense the intracellular metabolic homeostasis and stresses of four amino acids such as methionine, homocysteine, serine, and arginine as well as vitamin B12 and folate; second, similar to protein phosphorylation/ dephosphorylation-based signaling pathways, the metabolic homeostasis and stress signals relay the metabolic reprogramming signals into cellular methylation processes via various methyltransferases to methylate DNAs, proteins, histones, RNAs, and other molecules. The methylations of those important molecules regulate their biological functions; third, m 6 A-RNA methylation is a dominant RNA methylation for various RNA types including mRNAs, tRNAs, rRNAs, and noncoding RNAs. Throughout transcriptomic data analyses of 102 microarrays, RNA-Seq, and single-cell RNA-Seq related to 41 diseases in six categories organ failures, viral infections, metabolic diseases, acute inflammations, cancers, and autoimmune diseases, our data have demonstrated that several layers of regulatory systems regulate the transcriptomic changes of m 6 A-RMRs in diseases as well as pathophysiological conditions in various cell types, which include (1) cell surface receptors such as cytokine receptors, viral receptors, danger-associated molecular pattern (DAMP) receptor, pathogen-associated molecular pattern (PAMP) receptors, immune checkpoint receptors, and cosignaling receptors. Of note, immune checkpoint receptors and cosignaling receptors are the prototypic membrane protein interactions between cells; (2) cellular mechanisms such as macrophage polarization, endothelial cell activation, CD4 + Foxp3 + regulatory T cell activation, resting status, and pathophysiological changes of other cells; and (3) nuclear transcription factors (TFs) including proinflammatory TFs NF-κB, Jak-STATs, and tumor suppressors TP53, PTEN, and APC. In summary, our results have demonstrated that transcriptional regulations of m 6 A-RMRs are highly significant mechanisms in regulating m 6 A-RNA methylations related to various pathophysiological processes and diseases. Our findings provide novel insights on the roles of m 6 A-RMRs in the development of inflammatory disorders and malignancies as well as novel pathways for future therapeutic strategies for inflammatory diseases, sepsis, trauma, organ failures, autoimmune diseases, metabolic CVDs, and cancers.

Data Availability
All the datasets used in this study are publicly available. The analyzed results in this study are included within the article and Supplementary Materials.